Massimiliano de Leoni

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Data-aware process mining: discovering decisions in processes using alignments

Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Highly dynamic adaptation in process management systems through execution monitoring

Nowadays, process management systems can be used notonly in classical business scenarios, but also in highly mobile and dynamicsituations, e.g., in supporting operators during emergency managementin order to coordinate their activities. In such challenging situations,processes should be adapted, in order to cope with anomalous situations,including connection anomalies and task faults. In this paper, we presenta general approach, based on execution monitoring, which is (i) practical,by relying on well-established planning techniques, and (ii) does notrequire the definition of the adaptation strategy in the process itself(as most of the current approaches do). We prove the correctness andcompleteness of the approach.

Conformance checking of interacting processes with overlapping instances

The usefulness of process models (e.g., for analysis, improvement, or execution) strongly depends on their ability to describe reality. Conformance checking is a technique to validate how good a given process model describes recorded executions of the actual process. Recently, artifacts have been proposed as a paradigm to capture dynamic, and inter-organizational processes in a more natural way. Artifact-centric processes drop several restrictions and assumptions of classical processes. In particular, process instances cannot be considered in isolation as instances in artifact-centric processes may overlap and interact with each other. This significantly complicates conformance checking; the entanglement of different instances complicates the quantification and diagnosis of misalignments. This paper is the first paper to address this problem. We show how conformance checking of artifact-centric processes can be decomposed into a set of smaller problems that can be analyzed using conventional techniques.

Aligning event logs and process models for multi-perspective conformance checking: an approach based on integer linear programming

Modern organizations have invested in collections of descriptive and/or normative process models, but these rarely describe the actual processes adequately. Therefore, a variety of techniques for conformance checking have been proposed to pinpoint discrepancies between modeled and observed behavior. However, these techniques typically focus on the control-flow and abstract from data, resources and time. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data, time and resources). This way it is possible to quantify conformance and analyze differences between model and reality. The approach was implemented using ProM and has been evaluated using both synthetic event logs and a real-life case study.

Balanced multi-perspective checking of process conformance

Organizations maintain process models that describe or prescribe how cases (e.g., orders) are handled. However, reality may not agree with what is modeled. Conformance checking techniques reveal and diagnose differences between the behavior that is modeled and what is observed. Existing conformance checking approaches tend to focus on the control-flow in a process, while abstracting from data dependencies, resource assignments, and time constraints. Even in those situations when other perspectives are considered, the control-flow is aligned first, i.e., priority is given to this perspective. Data dependencies, resource assignments, and time constraints are only considered as “second-class citizens”, which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. Existing techniques will still diagnose the data-flow as deviating, whereas our approach will indeed point out that the control-flow is deviating. In this paper, a novel algorithm is proposed that balances the deviations with respect to all these perspectives based on a customizable cost function. Evaluations using both synthetic and real data sets show that a multi-perspective approach is indeed feasible and may help to circumvent misleading results as generated by classical single-perspective or staged approaches.

A General Framework for Correlating Business Process Characteristics

Process discovery techniques make it possible to automatically derive process models from event data. However, often one is not only interested in discovering the control-flow but also in answering questions like “What do the cases that are late have in common?”, “What characterizes the workers that skip this check activity?”, and “Do people work faster if they have more work?”, etc. Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for specific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. However, as shown in this paper, it is possible to unify these ideas and provide a general framework for deriving and correlating process characteristics. First, we show how the desired process characteristics can be derived and linked to events. Then, we show that we can derive the selected dependent characteristic from a set of independent characteristics for a selected set of events. This can be done for any process characteristic one can think of. The approach is highly generic and implemented as plug-in for the ProM framework. Its applicability is demonstrated by using it to answer to a wide range of questions put forward by the UWV (the Dutch Employee Insurance Agency).

Declarative process mining in healthcare

Our approach mediates between data reflecting the reality and clinical guidelines.We repair and enrich declarative process models based on event logs.Process mining techniques are used to check conformance and analyze deviations.We applied the approach in the Isala hospital in the Netherlands. Clinical guidelines aim at improving the quality of care processes through evidence-based insights. However, there may be good reasons to deviate from such guidelines or the guidelines may provide insufficient support as they are not tailored toward a particular setting (e.g., hospital policy or patient group characteristics). Therefore, we report a case study that shows how process mining techniques can be used to mediate between event data reflecting the clinical reality and clinical guidelines describing best-practices in medicine. Declarative models are used as they allow for more flexibility and are more suitable for describing healthcare processes that are highly unpredictable and unstable. Concretely, initial (hand made) models based on clinical guidelines are improved based on actual process executions (if these executions are proven to be correct). Process mining techniques can be also used to check conformance, analyze deviations, and enrich models with conformance-related diagnostics. The techniques have been applied in the urology department of the Isala hospital in the Netherlands. The results demonstrate that the techniques are feasible and that our toolset based on ProM and Declare is indeed able to provide valuable insights related to process conformance.

Data- and Resource-Aware Conformance Checking of Business Processes

Process mining is not restricted to process discovery and also includes conformance checking, i.e., checking whether observed behavior recorded in the event log matches modeled behavior. Many organizations have descriptive or normative models that do not adequately describe the actual processes. Therefore, a variety of techniques for conformance checking have been proposed. However, all of these techniques focus on the control-flow and abstract from data and resources. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data and resources). This way it is possible to quantify conformance and analyze differences between model and reality. The approach has been implemented in ProM and evaluated using a variety of model-log combinations.